End-to-End Horse Gait Classification in Uncontrolled Environments Using Inertial Sensors
Bibliographic record
Abstract
Locomotor injuries in horses are a major cause of underperformance and serious welfare issue. Veterinarians typically investigate horses’ lameness through visual examination at separate gaits (walk, trot, gallop). To evaluate lameness objectively, Inertial Measurement Units (IMU) based systems have been developed. It is necessary to accurately identify the gait of each stride as vertical displacement symmetry is assessed at a defined gait, essentially trot. This study aimed to classify gaits into 6 classes and to assess the training sample size required to maximize the performance. Unlike previous methods, we used raw IMU data without manually preselecting specific signal segments. Seven sensors were strategically placed on the limbs, head, withers, and pelvis of horses. 1440 horses were used in our unsupervised model and the gait of 110 horses was labelled using IMU data for our supervised models. We divided the 6 gaits classification task into two subtasks: a four-gaits classification and a gallop-specific classification. In the first subtask, we compared the performance of a machine learning (XGBoost), a deep learning (LSTM) and a transfer learning (ENCOD-CNN) model, depending on the labelled training sample size. Our results show that the transfer learning approach outperformed the other models, achieving test accuracy of 91.9%. Our gallop classification task achieves 97.1% accuracy and the total pipeline reaches 91.2% accuracy. Beyond improving gait classification in a real clinical setting, this research demonstrates the potential of transfer learning for time-series datasets and provides a quantitative assessment of the required labeled sample size for effective implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".